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Record W1481460984 · doi:10.1002/zoo.20403

The effect of feeding enrichment methods on the behavior of captive Western lowland gorillas

2011· article· en· W1481460984 on OpenAlexaff
Erin B. Ryan, Kathryn L. Proudfoot, David Fraser

Bibliographic record

VenueZoo Biology · 2011
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsForagingForageYardBiologyGorillaHayAnimal scienceEcologyCageMathematics

Abstract

fetched live from OpenAlex

Three feeding enrichment treatments were tested in an outdoor yard used by six Western lowland gorillas (Gorilla gorilla gorilla). In "Yard-toss," forage was thrown by hand over one third of the yard. In "Set-up," forage and browse were hand-scattered throughout the yard. "Set-up Enriched" was similar with the addition of either a hay- and forage-filled feeder or forage-filled boomer ball(s) suspended from a climbing structure. Each treatment was presented on 5 d. Behavior was recorded for 30 min before (baseline) and 30 min after the start of each treatment. All treatments led to more foraging and less inactivity compared with baseline (P80.05), but Yard-toss was the least effective, likely because resources were clumped and monopolized by dominant animals. In Set-up Enriched, dominant animals had the greatest increase in foraging (P=0.03), partly because they generally monopolized the suspended items, but this allowed others to forage at ground level. This separation of the animals likely explains why Set-Up Enriched led to more foraging than all other treatments (P80.05). Findings show that for these hierarchical animals, enrichment resources are most effective when distributed widely, including vertically, and that enrichment strategies must take social structure into account.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.402
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2011
Admission routes1
Has abstractyes

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